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ONLINE COVER Developing a Good Eye. Computer vision and robotics share the goal of creating systems that can understand their environments and interact with nearby objects. These systems often learn with data, such as photographs, selected by humans. Ideally, robotic agents would visually scan a scene and then autonomously identify important areas (such as a door frame or table edges). Ramakrishnan et al. used reinforcement learning to train an agent to automatically identify parts of images that allowed it to complete the rest. The authors then added a "sidekick" policy with additional data from partial views from different locations. The agent learned exploration behaviors that could be applied to new visual tasks. [CREDIT: SANTHOSH RAMAKRISHNAN/UNIVERSITY OF TEXAS (ROBOT: KIRILL MAKAROV/DREAMSTIME.COM]